Provisional Registration of a Retirement Fund under Scrutiny in Swaziland
Bibliographic record
Abstract
Abstract Recently the Industrial Court of Swaziland was faced with a complaint in Mngadi v Motor Vehicle Accident Fund’s Pension Fund, which raised two important issues of first impression in Swaziland retirement law. This note discusses the significance and effects of Mngadi on provisional registration of retirement funds in Swaziland. It argues that Mngadi should be welcomed because it clarifies the significance of the need for retirement funds to operate in accordance with their registered rules. The note also discusses the problems with the Registrar’s power to issue a provisional certificate of registration under Section 5 in light of the problems that emerged in Mngadi. The note argues that Mngadi should be welcomed because it highlights the characteristics of a defined benefit fund, and implicitly distinguishes it from a defined contribution fund. While Mngadi should generally be welcomed, the Industrial Court should be criticised for its failure to develop the law.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".